EP3457106A1 - Procédé et dispositif de surveillance de la cinématique d'un engrenage planétaire épicycloïdal - Google Patents

Procédé et dispositif de surveillance de la cinématique d'un engrenage planétaire épicycloïdal Download PDF

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Publication number
EP3457106A1
EP3457106A1 EP18189768.7A EP18189768A EP3457106A1 EP 3457106 A1 EP3457106 A1 EP 3457106A1 EP 18189768 A EP18189768 A EP 18189768A EP 3457106 A1 EP3457106 A1 EP 3457106A1
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EP
European Patent Office
Prior art keywords
planetary gear
model
pattern recognition
epicyclic
gear
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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EP18189768.7A
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German (de)
English (en)
Inventor
Dr. Sebastian NOWOISKY
Mateusz Grzeszkowski
Christoph Ende
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Rolls Royce Deutschland Ltd and Co KG
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Rolls Royce Deutschland Ltd and Co KG
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Publication of EP3457106A1 publication Critical patent/EP3457106A1/fr
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/021—Gearings
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16H—GEARING
    • F16H57/00—General details of gearing
    • F16H57/01—Monitoring wear or stress of gearing elements, e.g. for triggering maintenance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/028—Acoustic or vibration analysis
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02C—GAS-TURBINE PLANTS; AIR INTAKES FOR JET-PROPULSION PLANTS; CONTROLLING FUEL SUPPLY IN AIR-BREATHING JET-PROPULSION PLANTS
    • F02C7/00—Features, components parts, details or accessories, not provided for in, or of interest apart form groups F02C1/00 - F02C6/00; Air intakes for jet-propulsion plants
    • F02C7/36—Power transmission arrangements between the different shafts of the gas turbine plant, or between the gas-turbine plant and the power user
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16H—GEARING
    • F16H57/00—General details of gearing
    • F16H57/01—Monitoring wear or stress of gearing elements, e.g. for triggering maintenance
    • F16H2057/012—Monitoring wear or stress of gearing elements, e.g. for triggering maintenance of gearings
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16H—GEARING
    • F16H57/00—General details of gearing
    • F16H57/08—General details of gearing of gearings with members having orbital motion

Definitions

  • the invention relates to a method for monitoring the kinematics of an epicyclic planetary gear having the features of claim 1 and an apparatus for monitoring an epicyclic planetary gear having the features of claim 9.
  • Epicyclic planetary gears are widely used today.
  • the fan is decoupled from the turbine shaft using a planetary gear.
  • an increase in efficiency and a reduction in the noise emission can be achieved because the fan higher efficiency at low and the medium-pressure turbine only at very high Reached speeds.
  • the proportion of potential wear parts increases.
  • a monitoring of the kinematics of planetary gears is from the DE 10 2015 209 866 A1 known.
  • online monitoring of the kinematics of the planetary gear is also useful for diagnosing and forecasting gearbox damage.
  • a method for pattern recognition is used.
  • a model i.e., a calculation model
  • a vibration sensor device measures vibrations
  • the model thus obtained is used in a test phase for pattern recognition in vibration data of an epicyclic planetary gear to be tested, wherein angular position of at least one planetary gear in the epicyclic epicyclic gear to be tested is determined by the pattern recognition.
  • the pattern recognition in the test phase can determine the estimation of an initial angular position of the planetary gears. This estimate can then be used with the model to determine the angular positions of the epicyclic planetary gearset under test.
  • the model may include a hidden markov model and / or an artificial neural network.
  • the estimation of the initial angular position of the planetary gears is of interest because the initial angular position is not accessible by external measurements on the planetary gear.
  • the training phase extends at least over the time required for a meshing periodicity (n c, spr ), in particular over the time for an integer multiple of the meshing periodicity (n c, spr ).
  • a pre-processing step and / or a feature recognition step is performed, in which filtering of the vibration data, noise suppression of the vibration data and / or rotational-synchronous re-sampling, as used in the TSA algorithm.
  • a classification step is performed with a classifier, the result of the classification comprising an output probability matrix.
  • the classifier has a k-nearest neighbor classifier, a support vector machine and / or an artificial neural network, in particular a recurrent artificial neural network.
  • the k-nearest-neighbor classifier is one of the non-parametric classifiers that must be trained in a supervised learning process. This means that the class assignment of individual feature vectors to the individual state classes is known in the learning process.
  • a direct counting k of adjacent class representatives from the training phase takes place for each new, unknown feature vector in the feature space.
  • the learned k-nearest neighbor classifier then assigns the unknown feature vector to the class with the most representatives of a class.
  • a Viterbi algorithm is used in the test phase for determining a most probable angular state sequence (or discrete kinematic rotation angle state sequence), in particular, determining a gearing state most likely to correspond to the observed vibration signal patterns in the acquired vibration data.
  • the object is also achieved by a device for monitoring the kinematics of an epicyclic planetary gear to be tested with the features of claim 9.
  • the Fig. 1A to 1D each show schematically a per se known epicyclic planetary gear 2 with fixed ring gear H (see illustration in FIG Fig. 1A ) and planet gears P 1 , P 2 , P 3 .
  • the carrier also called planet carrier or web
  • the carrier which connects the planet gears P 1 , P 2 , P 3 firmly together, is not shown here for reasons of clarity.
  • an epicyclic planetary gear 2 with three planetary gears P 1 , P 2 , P 3 is chosen here purely by way of example. It can also be used more, especially five planetary gears. It is also not mandatory that with fixed ring gear H the drive takes place via the sun gear S and the output via the carrier. In principle, the drive or the output can also be made in each case by a pairing of ring gear H, sun gear S or Carrier. It is also possible in principle that each ring gear H, sun gear S or the carrier are fixed.
  • the structure-borne sound is the vibrations that propagate in the solid state of the epicyclic planetary gear 2, so that in the following also from vibration data is spoken.
  • a possible embodiment of a sensor device 1 has a piezoelectric sensor with which structure-borne noise can be detected.
  • the starting point for the embodiments, which are shown below, is the evaluation of the structure-borne noise detected on or in the epicyclic planetary gear 2.
  • accelerometers are suitable for detecting the state of transmission.
  • an acceleration sensor receives a complex amplitude-modulated oscillation signal which initially makes no conclusions as to possible transmission damage.
  • TSA Time Synchronous Averaging
  • Fig. 1A to 1D the first planetary gear P 1 is highlighted by a dark color. On the first planetary gear P 1 each have a tooth R P 1 is highlighted. On the sun gear in each case a tooth R S.
  • the continuously recorded sensor signals i.e., the vibration signals of the structure-borne sound
  • the continuously recorded sensor signals are first resampled in rotational synchronism and then divided into segments so that the segment length corresponds to the revolution period of a gear to be inspected.
  • the length of a segment corresponds to the period (f C -f P ) -1 (after Yu, Early Fault Detection for Gear Shaft and Planetary Gear Based on Wavelet and Hidden Markov Modeling, Dissertation, University of Toronto, 2011 ).
  • This segmentation is performed for each planetary gear P 1 , P 2 , P 3 ; so that the segments of a planetary gear P 1 , P 2 , P 3 can be assigned to the respective segment quantity v k as follows.
  • the time range of the vibration vibration signals when a planetary gear P 1 , P 2 , P 3 is in the vicinity of the sensor device 1, is evaluated in isolation from the time ranges, if no planetary gear P 1 , P 2 , P 3 in the vicinity of the sensor device 1 (see Ha et al., Autocorrelation-based synchronous averaging for condition monitoring of planetary gearboxes in wind turbines. In Mechanical Systems and Signal Processing, 70-71 (2016), pp. 161-175 and McFadden op. cit. ).
  • Fig. 2 On the left, the amplitude signal of the vibrations is plotted over three Tc cycles.
  • the vibration signals of the two segment quantities v 1 and v 3 for the two planetary gears P 1 , P 3 summed over the planetary rotation angle ⁇ p of the respective planet are plotted.
  • the fenestration of the vibration signals allows on the one hand a separation of the dominant vibration components of different gears and thus different rotational angular positions of the planetary gear and on the other hand can be reduced by means of the fenestration of the leak effect, which results in a further processing of the vibration signals in the Fourier spectrum.
  • the TSA method is used for the rotation angle synchronous averaging of the vibration data.
  • An averaging can be carried out with knowledge of the meshing periodicities independently of the initial angular position G 0 .
  • features are obtained from the individual windowed signals Use a pattern recognition system to detect local divergence of the planet tooth.
  • the separation efficiency of the obtained characteristics with respect to different damage severity must be evaluated in advance.
  • the evaluation should be carried out for different speed and load ranges, whereby several series of tests are performed on an epicyclic planetary gear 2.
  • the windowed signal sections between different measurements must be correctly assigned to the kinematic state of the epicyclic planetary gear 2.
  • the variables ⁇ S and ⁇ C can be sensed by means of an incremental encoder with zero pulse output on the drive shaft or the output shaft.
  • the planetary rotation angle ⁇ P is estimated from the remaining sensor data (ie the structure-borne sound or the vibration data) in order to be able to evaluate the characteristic values between different measurements. Without knowledge of the planetary rotation angle ⁇ P and a determination of the transmission initial rotational position G 0 from the measurement data is initially not possible.
  • the initial rotational position G 0 of the epicyclic planetary gear 2 can be determined, with pattern recognition being performed.
  • a pattern recognition 404 can use, for example, an artificial neural network (KNN), in particular a recurrent artificial neural network, a support vector machine (SVM) or also a hidden markov model (HMM).
  • KNN artificial neural network
  • SVM support vector machine
  • HMM hidden markov model
  • States of a system can be mapped as a stochastic process by so-called Markov chains.
  • the knowledge about the past states is used for the prediction of future states. How many states from the past are taken into account for the estimation of the future system state is given by the order of the Markov chain
  • the Fig. 3 schematically shows such a hidden Markov model.
  • the transition probabilities between the states are given in the matrix A and the probabilities of observing a certain emission v i in the state s i are given in the matrix B (output probability matrix).
  • Hidden Markov models have already been used to describe gear states ( Miao et al., A probabilistic description scheme for rotating machinery health evaluation. In: Journal of Mechanical Science and Technology 24 (12) (2010), pp. 2421-2430 ).
  • the degradation process of the transmission is described as a state in order to conclude from the observed data using an trained Markov model on the transmission state.
  • the states are distinguished error-free, error-prone or destroyed.
  • the embodiments described herein use Hidden Markov models for describing the kinematic state of the transmission (in particular, the Angular positions of a gear) of the epicyclic planetary gear 2 as part of a pattern recognition.
  • Fig. 3 describes a state s i a discrete rotational angular position of the epicyclic planetary gear 2 with the rotation angles ⁇ S , ⁇ P , ⁇ C in state i .
  • This state i emits, with the probability b i, vibration signal patterns from the output quantity V.
  • the element number of the state quantity and the output amount are coincident.
  • B is a diagonal matrix in this case.
  • the transition matrix A simplifies , as in Fig. 3 shown, since the meshing states of the transmission occur sequentially and thus all entries except for a i, i + 1 and a n c , spr , 1 to zero. Accordingly, this state sequence also emits a fixed sequence of vibration signal patterns.
  • the aim now is to analyze the emission sequence and to find the path through the state graph, which outputs the observed emission sequence according to the established Hidden Markov model. In other words, it searches for the sequence of gearing states most likely to be associated with the observed vibration signal patterns of a measurement. As soon as the state sequence is known, the initial angle position Go can be determined.
  • the Viterbi algorithm For uncovering the most probable state sequence of an observed emission sequence, the Viterbi algorithm (see eg Forney, The Viterbi algorithm. In: Proceedings of the IEEE, 61 (1973), No. 3, pp. 268-278 ) used become.
  • the Lazy Viterbi algorithm Feldman et al., A Fast Maximum Likelihood Decoder for Convolutional Codes; In: Proceedings of the Vehicular Technology Conference, 2002 Case, 2002 IEEE 56th
  • the forward algorithm, the backward algorithm, a posterior decoding, or a forward-backward algorithm such as the Baum-Welch algorithm.
  • a state transition diagram is first generated over all times and all states. Then, using the output probability matrix B of the Hidden Markov model for a given sequence in each node of the transition diagram, the probability of reaching that node from the parent node is calculated.
  • the most probable state path can be calculated for a given sequence, so that the concealed state can be assigned to each observed emission by subsequent backward readout of the nodes.
  • each meshing tooth engagement combination should have a high "uniqueness grade" in the vibration signal, so that subsequently the vibration signal patterns can be separated from each other in a separation-effective manner.
  • an evaluation of the emission sequence using the Viterbi algorithm offers the advantage that it is not necessary to distinguish all the observed oscillation signal patterns from one another, but it is sufficient if a part of the generated emission sequence can be differentiated from other emission sequence parts within one meshing periodicity. If so, an identification of the original state sequence can be successfully performed.
  • the embodiments for monitoring the kinematics of an epicyclic planetary gear 2 use an estimate of the initial angular position G 0 in connection with a pattern recognition.
  • the Fig. 4 represents processing stages of the pattern recognition system by way of example. This is in the sub-steps “training” (in Fig. 4 left) and “test” (in Fig. 4 right).
  • the training phase serves to form a model (i.e., a computational model) of the kinematics of epicyclic planetary gear 2, as is generally known in machine learning.
  • the data acquired by the vibration sensor device 1 can be filtered, for example, with a bandpass filter.
  • resonance vibrations can be filtered out in order to improve the separation efficiency for the features in the subsequent steps.
  • a noise suppression can be performed. Also it is possible e.g. to smooth the measured data.
  • step 402 from the windowed vibration measurement data (see Fig. 2 and the associated description) appropriate feature expression (eg, and averaging, standard deviation, kurtosis of preprocessed raw data) obtained (step 402), which is also referred to as feature extraction.
  • feature expression eg, and averaging, standard deviation, kurtosis of preprocessed raw data
  • step 402 one-dimensional characteristics, which show characteristic feature values in the case of specific tooth states, are calculated from one-dimensional or multidimensional signal representations in the rotation angle range or time-frequency range.
  • step 403 it is possible that all determined features of equal significance are included in the subsequent training of the classifier (step 403). However, it is also possible for a weighting of the features to be carried out in accordance with a defined quality function (for example the F ratio). If the determined features have a speed frequency dependency, this information could be taken into account in the feature extraction.
  • a defined quality function for example the F ratio
  • the classifier eg an SVM or an ANN
  • the rotation angle data ⁇ S , ⁇ P , ⁇ C in this case allow an exact assignment of an observed vibration signal pattern to a state class.
  • a weighting can be made. For example, window signals that were detected with greater uncertainty than others (eg because they are closer to a cluster boundary) can be used with a correspondingly lower weighting.
  • a vector would be created next to the vector with the observations, which describes the safety of each observation. This additional information could then also be introduced at a suitable location in the Viterbi algorithm (step 414, see below).
  • step 4114 it is possible to use the Viterbi algorithm (step 414) to search for a segment in which the classification was most likely to work very well. Such a section would be e.g. notice that many consecutive individual observations occur.
  • the output probability matrix B can be used to describe states which have a low degree of "uniqueness” in the case of a specific tooth engagement.
  • the misclassified observations in the training data are evaluated.
  • the Hidden Markov model can be built (step 404).
  • the pattern recognition system is considered trained; i.e. there is a trained model in the form of a computer model (Classificator 403, Hidden Markov Model 404), which is useful for detecting transmission damage in tests (Steps 413, 414).
  • the use of the model in the test phase is temporally subordinate to the training phase of the computer model and can then be used as often as desired.
  • test data (right side in Fig. 4 ), ie kinematic data of the epicyclic planetary gear 2, processed, which do not include angle data.
  • the system can then independently calculate the state sequence from this.
  • step 411 the first two processing stages with preprocessing (step 411) and feature extraction (step 412) are analogous to the training process (left side Fig. 4 ).
  • the classifier (step 413) assigns each oscillation signal window to an element of the observation set V , so that an emission sequence results for an entire measurement.
  • step 414) the Viterbi algorithm is applied to the emission sequence and the most likely original state sequence is revealed. If the state sequence is known, finally the initial angular position G 0 of the planetary gear 2 can be calculated.
  • the idea of the measuring method is based on the magnetic detection of the planetary track and the zero angle position of the sun gear S.
  • a permanent magnet with low intrinsic mass and high magnetic remanence on the one hand on the planetary gear P 1 , P 2 , P 3 is arranged in the point M P in the practical realization , so that this fixed point during rotation of the planetary gear P 1 , P 2 , P 3 has a star-shaped path.
  • a permanent magnet on the sun gear S is arranged at the point M S for detecting the sun gear zero angle position.
  • Footbridge turns his starting position. This starting point of the magnet or the rotational angle zero point of the planetary gear P 1 , P 2 , P 3 is scanned magnetically.
  • the sampling point H 1 serves to reliably identify the M p path portion which is reached by the permanent magnet when passing through its starting position. However, the sampling point H 1 is passed at a lower speed, whereby the magnetic flux density at this point has a broad peak in the time domain signal (see Fig. 6 ).
  • a maximum of the magnetic flux density can not be specified exactly, whereby a maximum value and thus also the exact time when magnet M p passes sensor H 1 , with a higher uncertainty is afflicted.
  • the sampling point H 2 was introduced, which is passed by the planetary point Mp at a higher speed than H 1 . Therefore, the magnetic flux density at this point has a narrower peak in the time domain signal and the maximum can be determined with less uncertainty. Now passes through the marked planetary point these two Sampling points H 1 and H 2 then the M P path can be reliably detected every n c, pr land revolutions.
  • the sensors H 1 and H 2 realize a detection of the timing of the same tooth engagement between Planetenradzähnen and Hohlradzähnen.
  • the meshing of the planetary gears P 1 , P 2 , P 3 with the sun gear S must also be taken into account. This is done by sampling the sun gear angular position with another permanent magnet M s and a linear Hall sensor at location H 3 (x, y) (see also FIG Fig. 5 ) reached.
  • Fig. 5 the position of the permanent magnet M s is indicated as a function of the angle of rotation of the carrier C.
  • Fig. 6 are the timing waveforms of the three Hall sensors H 1 , H 2 , H 3 when passing the two magnets M p and Ms shown. This shows that the sensor H 2 is better suited to the sensor H 1 for the determination of the initial tooth engagement time G 0 due to the narrower peak.
  • the Fig. 7 shows the assignment of the observed emission sequence over two "meshing periodicities" to the individual meshing states by the pre-trained classifier.
  • sPr 70 land revolutions and a total of three planetary gears P 1 , P 2 , P 3, a total of 210 states s i were defined within the two "tooth engagement periodicities".
  • a discrete state always describes the time when one of the planet gears P 1 , P 2 , P 3 is located below an acceleration sensor device 1 attached to the ring gear H.
  • the representation in Fig. 7 shows the states assigned by the classifier over the real states s i .
  • Y (s i ) see Fig. 3 , Output of the classifier in step 413) would change in the chosen representation in Fig. 7 make a straight with the slope of one.
  • the Fig. 8 shows the average success rate of all measurement series at variable speed and load with respect to the estimated initial angular position G 0 . This sets the output of step 415 in FIG Fig. 4 represents.
  • the median success rate is in Fig. 8 is plotted as a parameter as a function of the starting point s i of the initial state (y-axis) and the length of observed state sequence (x-axis).
  • the evaluation achieves a high success rate in the assignment of the initial angle position Go using the presented method.
  • the developed pattern recognition system has a certain degree of robustness against misclassifications.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Mechanical Engineering (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
EP18189768.7A 2017-09-13 2018-08-20 Procédé et dispositif de surveillance de la cinématique d'un engrenage planétaire épicycloïdal Withdrawn EP3457106A1 (fr)

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DE102017121239.6A DE102017121239A1 (de) 2017-09-13 2017-09-13 Verfahren und Vorrichtung zur Überwachung der Kinematik eines epizyklischen Planetengetriebes

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CN112231859B (zh) * 2020-10-11 2022-05-17 昆明理工大学 一种行星齿轮箱行星轮局部故障振动仿真方法
CN112257528B (zh) * 2020-10-12 2023-07-18 南京工业大学 一种基于小波变换和密集连接扩张卷积神经网络的风电齿轮箱故障诊断方法
CN113343477A (zh) * 2021-06-23 2021-09-03 中国华能集团清洁能源技术研究院有限公司 双馈风电机组传动系统扭振响应计算方法、装置及存储介质
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DE102017121239A1 (de) 2019-03-14

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